Raouf Hamzaoui

Professor Raouf Hamzaoui

Job: Professor in Media Technology

Faculty: Computing, Engineering and Media

School/department: School of Engineering, Infrastructure and Sustainability

Research group(s): Institute of Engineering Sciences

Address: 羞羞视频, The Gateway, Leicester, LE1 9BH

T: +44 (0)116 207 8096

E: rhamzaoui@dmu.ac.uk

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Personal profile

Raouf Hamzaoui received the MSc degree in mathematics from the University of Montreal, Canada, in 1993, the Dr.rer.nat. degree from the University of Freiburg, Germany, in 1997 and the Habilitation degree in computer science from the University of Konstanz, Germany, in 2004. He was an Assistant Professor with the Department of Computer Science of the University of Leipzig, Germany and with the Department of Computer and Information Science of the University of Konstanz. In September 2006, he joined 羞羞视频 where he is a Professor in Media Technology. Raouf Hamzaoui is an IEEE Senior member. He was a member of the Editorial Board of the IEEE Transactions on Multimedia and IEEE Transactions on Circuits and Systems for Video Technology. He has published more than 130 research papers in books, journals, and conferences. His research has been funded by the EU, DFG, Royal Society, Chinese Academy of Sciences, China Ministry of Science and Technology, and industry and received best paper awards (ICME 2002, PV’07, CONTENT 2010, MESM’2012, UIC-2019, CCF Transactions on Pervasive Computing and Interaction 2020).

Research group affiliations

Institute the Institute for Sustainable Futures

Publications and outputs

dc.title: Mobility-Resilient Datalink Protocol for Next-Generation Aeronautical Communication dc.contributor.author: 脰zmen, Sergun; Hamzaoui, Raouf; Louadah, Hassna; Chen, Feng; Cetek, Fulya Aybek; Arnaldo Valdes, Rosa; Reinaldos Manzanares, Pedro; Vicente Martin, Guillermo; Regnault, Michael dc.description.abstract: This paper presents the design and analysis of a mobility-resilient datalink communication protocol to meet the demands of next-generation aeronautical communication over the Aeronautical Telecommunication Network/Internet Protocol Suite (ATN/IPS). The protocol was developed under the Horizon Europe SESAR JU-funded ATMACA (Air Traffic Management and Communication over ATN/IPS) project. The project responds to the increasing demand for seamless, resilient, and interoperable air-ground communication in environments that involve frequent changes in geography, connectivity, and operational roles. The protocol introduces new capabilities that overcome limitations in existing mobility or session management protocols, such as Proxy Mobile IPv6 and Session Initiation Protocol (SIP). It includes mechanisms for adaptive routing, fault recovery, and network reconfiguration. These mechanisms follow software-defined networking principles and support the delivery of uninterrupted air-ground communication, including Controller鈥揚ilot Data Link Communications (CPDLC) messages, across changing conditions. The architecture of the protocol consists of three integrated logical layers: transport, session, and context. Each layer addresses a specific aspect of mobility. The transport layer establishes physical connections, the session layer maintains logical application-level continuity, and the context layer tracks user state, roles, and operational conditions. Together, these layers provide support for terminal, session, service, and user mobility. The architecture includes several software-defined roles. These roles include Air Traffic Management (ATM) Servers, Air Traffic Control (ATC) Agents, and Context Management Agents. Each role has clearly defined responsibilities related to provisioning, mobility coordination, and session management. The network architecture is organized hierarchically across sector, facility, area, and flight information region levels, supporting both local and cross-regional coordination. This hierarchy supports distributed control and coordination across the entire airspace. A core feature of the protocol is the DataLink Information eXchange (DIX) format. This binary message format supports structured communication between agents, clients, and services. It encodes application data, session identifiers, context metadata, and mobility events. The format reduces signaling overhead and allows fast propagation of events and updates. The structure of DIX messages supports asynchronous communication and real-time adaptability. This approach ensures compatibility with legacy systems while enabling integration with new air traffic management services. The protocol uses a hierarchical addressing model that assigns operational meaning to each network identifier. This model allows intelligent routing and scalable service discovery. The addressing scheme maps directly to roles and responsibilities in airspace operations. Each node receives a unique address that reflects its location, organizational affiliation, and function. These identifiers enable precise routing and facilitate cross-domain interoperability. The protocol supports both direct peer-to-peer connections and logical session-based communication. This dual-mode capability allows flexibility in deployment. The protocol works across satellite, terrestrial, and airport-based communication systems. It also supports transitions between different access technologies without interrupting ongoing services. The network can operate in centralized or federated modes, depending on the needs of the air traffic service provider. The design also improves scalability and simplifies integration with other SESAR and ICAO systems. In conclusion, the ATMACA protocol provides a robust and scalable communication framework. It addresses existing shortcomings in datalink systems and supports future development in global air traffic management infrastructure. dc.description: The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.
dc.title: FD-SCU: Frequency Decomposition-based Spectrum Collaborative Upsampling for Point Cloud Color Attribute dc.contributor.author: Hao, Liu; Wang, Wenchao; Yuan, Hui; Hamzaoui, Raouf; Yan, Weiqing; Hou, Junhui dc.description.abstract: Existing point cloud color upsampling methods typically treat color upsampling as an interpolation problem within a local color or implicit feature domain. This largely overlooks the ability of the frequency domain to capture color correlations in local point sets. To address this limitation, we propose a spectrum collaborative strategy that uses frequency decomposition on voxel blocks (VBs) to enhance point cloud color reconstruction. We first voxelize the low-resolution (LR) color point cloud to generate multiple VBs and introduce a virtual filling strategy that adaptively assigns colors to empty voxels in each VB, ensuring that the irregularly distributed color information fully occupies the VB. We then apply the discrete cosine transform, known for its strong frequency-domain representation of locally smooth signals, to each color-filled VB to obtain frequency coefficients. These frequency coefficients are separated into high-frequency (HF) and low-frequency (LF) components. The LF coefficients, together with the LR color point cloud, are fed into a multi-scale cross-domain feature extraction module to capture deep features. Next, a Gaussian perturbation-based feature expansion generates upsampled color features, which are used to regress a coarse upsampled color point cloud. Finally, a high-frequency-guided residual refinement module uses the HF coefficients to refine the coarse upsampled result and produce a high-fidelity color point cloud. Extensive experiments demonstrate that our method achieves superior performance compared to state-of-the-art methods. Our code will be publicly available at https://github.com/wangwenchaoxx/FD-SCU. dc.description: The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.
dc.title: Feature Compression for Cloud-Edge Multimodal 3D Object Detection dc.contributor.author: Tian, Chongzhen; Li, Zhengxin; Yuan, Hui; Hamzaoui, Raouf; Shen, Liquan; Kwong, Sam dc.description.abstract: Machine vision systems, which can efficiently manage extensive visual perception tasks, are becoming increasingly popular in industrial production and daily life. Due to the challenge of simultaneously obtaining accurate depth and texture information with a single sensor, multimodal data captured by cameras and LiDAR is commonly used to enhance performance. Additionally, cloud-edge cooperation has emerged as a novel computing approach to improve user experience and ensure data security in machine vision systems. This paper proposes a pioneering solution to address the feature compression problem in multimodal 3D object detection. Given a sparse tensor-based object detection network at the edge device, we introduce two modes to accommodate different application requirements: Transmission-Friendly Feature Compression (T-FFC) and Accuracy-Friendly Feature Compression (A-FFC). In T-FFC mode, only the output of the last layer of the network鈥檚 backbone is transmitted from the edge device. The received feature is processed at the cloud device through a channel expansion module and two spatial upsampling modules to generate multiscale features. In A-FFC mode, we expand upon the T-FFC mode by transmitting two additional types of features. These added features enable the cloud device to generate more accurate multiscale features. Experimental results on the KITTI dataset using the VirConv-L detection network showed that T-FFC was able to compress the features by a factor of 4933 with less than a 3% reduction in detection performance. On the other hand, A-FFC compressed the features by a factor of about 733 with almost no degradation in detection performance. We also designed optional residual extraction and 3D object reconstruction modules to facilitate the reconstruction of detected objects. The reconstructed objects effectively reflected the shape, occlusion, and details of the original objects. Our source code is released on GitHub at: https://github.com/yuanhui0325/FC3DOD. dc.description: The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.
dc.title: UGAE: Unified Geometry and Attribute Enhancement for G-PCC Compressed Point Clouds dc.contributor.author: Zhao, Pan; Yuan, Hui; Tian, Chongzhen; Guo, Tian; Hamzaoui, Raouf; Pan, Zhigeng dc.description.abstract: Lossy compression of point clouds reduces storage and transmission costs; however, it inevitably leads to irreversible distortion in geometry structure and attribute information. To address these issues, we propose a unified geometry and attribute enhancement (UGAE) framework, which consists of three core components: post-geometry enhancement (PoGE), pre-attribute enhancement (PAE), and post-attribute enhancement (PoAE). In PoGE, a Transformer-based sparse convolutional U-Net is used to reconstruct the geometry structure with high precision by predicting voxel occupancy probabilities. Building on the refined geometry structure, PAE introduces an innovative enhanced geometry-guided recoloring strategy, which uses a detail-aware K-Nearest Neighbors (DA-KNN) method to achieve accurate recoloring and effectively preserve high-frequency details before attribute compression. Finally, at the decoder side, PoAE uses an attribute residual prediction network with a weighted mean squared error (W-MSE) loss to enhance the quality of high-frequency regions while maintaining the fidelity of low-frequency regions. UGAE significantly outperformed existing methods on three benchmark datasets: 8iVFB, Owlii, and MVUB. Compared to the latest G-PCC test model (TMC13v29), in terms of total bitrate setting, UGAE achieved an average BD-PSNR gain of 9.98 dB and -90.54% BD-bitrate for geometry under the D1 metric, as well as a 3.34 dB BD-PSNR improvement with -55.53% BD-bitrate for attributes. Additionally, it improved perceptual quality significantly. Our source code will be released on GitHub at: https://github.com/yuanhui0325/UGAE dc.description: The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.
dc.title: PCE-GAN: A Generative Adversarial Network for Point Cloud Attribute Quality Enhancement based on Optimal Transport dc.contributor.author: Guo, Tian; Yuan, Hui; Liu, Qi; Su, Honglei; Hamzaoui, Raouf; Kwong, Sam dc.description.abstract: Point cloud compression significantly reduces data volume but sacrifices reconstruction quality, highlighting the need for advanced quality enhancement techniques. Most existing approaches focus primarily on point-to-point fidelity, often neglecting the importance of perceptual quality as interpreted by the human visual system. To address this issue, we propose a generative adversarial network for point cloud quality enhancement (PCE-GAN), grounded in optimal transport theory, with the goal of simultaneously optimizing both data fidelity and perceptual quality. The generator consists of a local feature extraction (LFE) unit, a global spatial correlation (GSC) unit and a feature squeeze unit. The LFE unit uses dynamic graph construction and a graph attention mechanism to efficiently extract local features, placing greater emphasis on points with severe distortion. The GSC unit uses the geometry information of neighboring patches to construct an extended local neighborhood and introduces a transformer-style structure to capture long-range global correlations. The discriminator computes the deviation between the probability distributions of the enhanced point cloud and the original point cloud, guiding the generator to achieve high quality reconstruction. Experimental results show that the proposed method achieves state-of-the-art performance. Specifically, when applying PCE-GAN to the latest geometry-based point cloud compression (G-PCC) test model, it achieves an average BD-rate of -19.2% compared with the PredLift coding configuration and -18.3% compared with the RAHT coding configuration. Subjective comparisons show a significant improvement in texture clarity and color transitions, revealing finer details and more natural color gradients. dc.description: The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.
dc.title: PU-GSM: A Latent Geometry-Guided Self-Similarity Model for Point Cloud Upsampling dc.contributor.author: Liu, Hao; Yuan, Hui; Hamzaoui, Raouf; Yan, Weiqing dc.description.abstract: Existing point cloud upsampling methods typically treat upsampling as a local interpolation problem, neglecting the importance of global correlations within point sets, which can limit their performance. To address this limitation, we exploit the inherent self-similarity of point clouds from a global perspective and propose PU-GSM, a latent geometry-guided self-similarity model for upsampling. We first generate a lower-resolution sparse sub-point cloud (SPC) by downsampling the input point cloud (IPC). Then, we introduce a latent geometry-guided selfsimilarity model (LGSM) that learns a point distribution on the underlying surface of SPC by exploiting the inherent self-similarity of IPC. Next, we reuse the LGSM for the remaining points (i.e., the points left after removing SPC from IPC). Afterward, we introduce a gradient-aware dual domain refiner to generate and calibrate the upsampled point cloud from the learned point distribution. Finally, we propose an inference-free latent vector matching approach to regularize the upsampled point cloud by enhancing the feature similarity between the upsampled point cloud and the ground truth in latent space. Extensive experiments show that PU-GSM achieves better upsampling results compared to state-of-the-art methods. Our code will be available at: https://github.com/liuhaoyun/PU-GSM. dc.description: The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.
dc.title: VP-JND: Visual Perception Assisted Deep Picture-Wise Just Noticeable Difference Prediction Model for Image Compression dc.contributor.author: Zhang, Yun; Zhang, Shisheng; Li, Na; Fan, Chunling; Hamzaoui, Raouf dc.description.abstract: The Picture-Wise Just Noticeable Difference (PWJND) represents the visibility threshold of human vision when viewing distorted images. The PW-JND plays an important role in perceptual image processing and compression. However, predicting the PW-JND is challenging due to its dependence on image content, viewing conditions, and the viewer. In this paper, we propose a visual perception-assisted deep PW-JND (VP-JND) prediction model for image compression that combines data-driven methods with the perceptual mechanisms of human vision. First, we identify a correlation between PW-JND and conventional pixel-wise JND. Based on this observation, we design the VP-JND model, consisting of a pixel-wise JND model, a deep binary classifier (VP-JNDnet) and a binary block search algorithm for refining predictions. VP-JNDnet exploits the pixelwise JND map of the original image to predict whether a compressed image is perceptually lossless. In addition, the model incorporates visual importance of content and regions by using a mixed attention module and calculating perceptual loss during training. Experimental results show that VP-JND achieved an average precision of 94.82% and a mean absolute difference of 3.92 in predicting the JPEG quality factor corresponding to the PW-JND on the MCL-JCI dataset, outperforming state-of-the-art JND models. When applied to perceptual lossless image coding, the predicted PW-JND enabled average bit rate savings of 89.35% for JPEG compression on MCL-JCI and 85.46%/41.13% for JPEG/BPG compression on KonJND-1k. These savings were relative to images compressed at the lowest distortion level. The source codes and trained models are publicly available at https://github.com/SYSU-Video/VP-JND. dc.description: The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.
dc.title: CS-Net: Contribution-based Sampling Network for Point Cloud Simplification dc.contributor.author: Guo, Tian; Chen, Chen; Yuan, Hui; Mao, Xiaolong; Hamzaoui, Raouf; Hou, Junhui dc.description.abstract: Point cloud sampling plays a crucial role in reducing computation costs and storage requirements for various vision tasks. Traditional sampling methods, such as farthest point sampling, lack task-specific information and, as a result, cannot guarantee optimal performance in specific applications. Learning-based methods train a network to sample the point cloud for the targeted downstream task. However, they do not guarantee that the sampled points are the most relevant ones. Moreover, they may result in duplicate sampled points, which requires completion of the sampled point cloud through post-processing techniques. To address these limitations, we propose a contribution-based sampling network (CS-Net), where the sampling operation is formulated as a Top-k operation. To ensure that the network can be trained in an end-to-end way using gradient descent algorithms, we use a differentiable approximation to the Top-k operation via entropy regularization of an optimal transport problem. Our network consists of a feature embedding module, a cascade attention module, and a contribution scoring module. The feature embedding module includes a specifically designed spatial pooling layer to reduce parameters while preserving important features. The cascade attention module combines the outputs of three skip connected offset attention layers to emphasize the attractive features and suppress less important ones. The contribution scoring module generates a contribution score for each point and guides the sampling process to prioritize the most important ones. Experiments on the ModelNet40 and PU147 showed that CS-Net achieved state-of-the-art performance in two semantic-based downstream tasks (classification and registration) and two reconstruction-based tasks (compression and surface reconstruction). CS-Net also achieved high average precision for objection detection on the KITTI LiDAR point cloud dataset, demonstrating its effectiveness in three-dimensional object detection.
dc.title: SPAC: Sampling-based Progressive Attribute Compression for Dense Point Clouds dc.contributor.author: Mao, Xiaolong; Yuan, Hui; Guo, Tian; Jiang, Shiqi; Hamzaoui, Raouf; Kwong, Sam dc.description.abstract: We propose an end-to-end attribute compression method for dense point clouds. The proposed method combines a frequency sampling module, an adaptive scale feature extraction module with geometry assistance, and a global hyperprior entropy model. The frequency sampling module uses a Hamming window and the Fast Fourier Transform to extract high-frequency components of the point cloud. The difference between the original point cloud and the sampled point cloud is divided into multiple sub-point clouds. These sub-point clouds are then partitioned using an octree, providing a structured input for feature extraction. The feature extraction module integrates adaptive convolutional layers and uses offset-attention to capture both local and global features. Then, a geometry-assisted attribute feature refinement module is used to refine the extracted attribute features. Finally, a global hyperprior model is introduced for entropy encoding. This model propagates hyperprior parameters from the deepest (base) layer to the other layers, further enhancing the encoding efficiency. At the decoder, a mirrored network is used to progressively restore features and reconstruct the color attribute through transposed convolutional layers. The proposed method encodes base layer information at a low bitrate and progressively adds enhancement layer information to improve reconstruction accuracy. Compared to the best anchor of the latest geometry-based point cloud compression (G-PCC) standard that was proposed by the Moving Picture Experts Group (MPEG), the proposed method can achieve an average Bj酶ntegaard delta bitrate of -24.58% for the Y component (resp. -21.23% for YUV components) on the MPEG Category Solid dataset and -22.48% for the Y component (resp. -17.19% for YUV components) on the MPEG Category Dense dataset. This is the first instance that a learning-based attribute codec outperforms the G-PCC standard on these datasets by following the common test conditions specified by MPEG. Our source code will be made publicly available on https://github.com/sduxlmao/SPAC. dc.description: The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.
dc.title: Air Traffic Management and Communication over ATN/IPS for Future Datalink Communication dc.contributor.author: Aydo臒an, Emre; 脰zmen, Sergun; Cetek, Fulya Aybek; Arnaldo Vald茅s, Rosa Mar铆a; Delgado-Aguilera Jurado, Raquel; Carmona Fern谩ndez, 脕ngel Ernesto; Mart铆nez Miralles, Adri谩n; Vendruscolo, Tommaso; Bonelli, Stefano; Delahaye, Daniel; Chaimatanan, Supatcha; Chen, Feng; Hamzaoui, Raouf dc.description.abstract: The growing demand for air traffic presents challenges in air traffic management, making seamless gate-to-gate communication essential. Traditional radio frequency communication faces limitations such as weather dependency and frequency restrictions. To address these issues, data link communications have gained importance, using VHF channels, satellite systems, and ATN/IPS-based networks. This study introduces the ATMACA (Air Traffic Management and Communication Over ATN/IPS) protocol, an advanced context management framework for ATN/IPS, designed to enhance aviation communications. ATMACA integrates instant messaging and software-defined nodes to improve connectivity, session continuity, and mobility management across networks and devices. It ensures seamless user interaction, reduces pilot workload, and enhances flight safety through automated Air Traffic Control (ATC) sector handoff in Controller鈥揚ilot Data Link Communications (CPDLC) and Data Link Initiation Capability (DLIC) applications. Another key innovation of the ATMACA framework is Green Route Operations (GRO), which enables real-time trajectory prediction and optimization.

Key research outputs

H. Liu, H. Yuan, J. Hou, R. Hamzaoui, W. Gao, PUFA-GAN: A Frequency-Aware Generative Adversarial Network for 3D Point Cloud Upsampling, IEEE Transactions on Image Processing, vol. 31, pp. 7389-7402, 2022, doi: 10.1109/TIP.2022.3222918.
Q. Liu, H. Yuan, J. Hou, R. Hamzaoui, H. Su, Model-based joint bit allocation between geometry and color for video-based 3D point cloud compression, IEEE Transactions on Multimedia, vol. 23, pp. 3278-3291, 2021, doi: 10.1109/TMM.2020.3023294.
Ahmad, S., Hamzaoui, R., Al-Akaidi, M., Adaptive unicast video streaming with rateless codes and feedback, IEEE Transactions on Circuits and Systems for Video Technology, vol. 20, pp. 275-285, Feb. 2010.

Röder, M., Cardinal, J., Hamzaoui, R., Efficient rate-distortion optimized media streaming for tree-structured packet dependencies, IEEE Transactions on Multimedia, vol. 9, pp. 1259-1272, Oct. 2007.

Röder, M., Hamzaoui, R., Fast tree-trellis list Viterbi decoding, IEEE Transactions on Communications, vol. 54, pp. 453-461, March 2006.

Röder, M., Cardinal, J., Hamzaoui, R., Branch and bound algorithms for rate-distortion optimized media streaming, IEEE Transactions on Multimedia, vol. 8, pp. 170-178, Feb. 2006.

Stankovic, V., Hamzaoui, R., Xiong, Z., Real-time error protection of embedded codes for packet erasure and fading channels, IEEE Transactions on Circuits and Systems for Video Technology, vol. 14, pp. 1064-1072, Aug. 2004.

Stankovic, V., Hamzaoui, R., Saupe, D., Fast algorithm for rate-based optimal error protection of embedded codes, IEEE Transactions on Communications, vol. 51, pp. 1788-1795, Nov. 2003.

Hamzaoui, R., Saupe, D., Combining fractal image compression and vector quantization, IEEE Transactions on Image Processing, vol. 9, no. 2, pp. 197-208, 2000.

Hamzaoui, R., Fast iterative methods for fractal image compression, Journal of Mathematical Imaging and Vision 11,2 (1999) 147-159.

Research interests/expertise

Image and Video Compression
Multimedia Communication
Error Control Systems
Image and Signal Processing
Machine Learning
Pattern Recognition
Algorithms

Areas of teaching

Signal Processing

Image Processing

Data Communication

Media Technology

Qualifications

Master’s in Mathematics (Faculty of Sciences of Tunis), 1986

MSc in Mathematics (University of Montreal), 1993

Dr.rer.nat (University of Freiburg), 1997

Habilitation in Computer Science (University of Konstanz), 2004

Courses taught

Digital Signal Processing

Mobile Communication

Communication Networks

Signal Processing

Multimedia Communication

Digital Image Processing

Mobile Wireless Communication

Research Methods

Pattern Recognition

Error Correcting Codes

Honours and awards

Outstanding Associate Editor Award, IEEE Transactions on Multimedia, 2020

Certificate of Merit for outstanding editorial board service, IEEE Transactions on Multimedia, 2018

Best Associate Editor award, IEEE Transactions on Circuits and Systems for Video Technology, 2014

Best Associate Editor award, IEEE Transactions on Circuits and Systems for Video Technology, 2012

Membership of professional associations and societies

IEEE Senior Member

IEEE Signal Processing Society

IEEE Multimedia Communications Technical Committee

British Standards Institute (BSI) IST/37 committee

Current research students

Sergun Ozmen, PT PhD student since July 2019

Professional esteem indicators

Guest Editor , Electronics Letters, 2024.

Guest Editor IEEE Open Journal of Circuits and Systems, Special Section on IEEE ICME 2020.

Guest Editor IEEE Transactions on Multimedia, Special Issue on Hybrid Human-Artificial Intelligence for Multimedia Computing.

Editorial Board Member Frontiers in Signal Processing (2021-)

Editorial Board Member IEEE Transactions on Multimedia (2017-2021)

Editorial Board Member IEEE Transactions on Circuits and Systems for Video Technology (2010-2016)

Co-organiser Special Session on 3D Point Cloud Acquisition, Processing and Communication (3DPC-APC), 2022 IEEE International Conference on Visual Communications and Image Processing (VCIP) December 13 – 16, 2022, Suzhou, China.

Co-organiser 1st International Workshop on Advances in Point Cloud Compression, Processing and Analysis, at ACM Multimedia 2022, Lisbon, Oct. 2022.

Area Chair 27th IEEE International Workshop on Multimedia Signal Processing (MMSP 2025), Beijing, Sep. 2025.

Area Chair IEEE International Conference on Image Processing (ICIP) 2025, Anchorage, Sept. 2025.

Area Chair IEEE International Conference on Multimedia and Expo (ICME) 2025, Nantes, June-July 2025.

Area Chair IEEE International Conference on Image Processing (ICIP) 2024, Abu Dhabi, Oct. 2024.

Area Chair IEEE International Conference on Multimedia and Expo (ICME) 2024, Niagara Falls, July 2024.

Area Chair IEEE International Conference on Image Processing (ICIP) 2023, Kuala Lumpur, Oct. 2023.

Area Chair IEEE International Conference on Multimedia and Expo (ICME) 2023, Brisbane, July 2023.

Area Chair IEEE International Conference on Image Processing (ICIP) 2022, Bordeaux, October 2022.

Area Chair for Multimedia Communications, Networking and Mobility IEEE International Conference on Multimedia and Expo (ICME) 2022, Taipei, July 2022.

Area Chair, IEEE ICIP 2021, Anchorage, September 2021

Area Chair for Multimedia Communications, Networking and Mobility, IEEE ICME 2021, Shenzhen, July 2021

Workshops Co-Chair, IEEE ICME 2020, London, July 2020.

Technical Program Committee Co-Chair, IEEE MMSP 2017, London-Luton, Oct. 2017.